Search Results (131)

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developing precise real-time icing grids is challenging due to the uneven distribution of monitoring stations, data confidentiality restrictions, and the limitations of existing interpolation methods. In this study, we propose a new approach for constructing real-time icing grid fields using 1339 online terminal monitoring datasets provided by the China Southern Power Grid Research Institute Co., Ltd. (CSPGRI) during the winter of 2023. Our method integrates static geographic information, dynamic meteorological factors, and ice_kriging values derived from parameter-optimized Empirical Bayesian Kriging Interpolation (EBKI) to create a spatiotemporally matched, multi-source fused icing thickness grid dataset. We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. The stacking model performed best, achieving an R-value of 0.634 (0.893), RMSE of 3.424 mm (2.834 mm), CSI of 0.514 (0.774), MAR of 0.309 (0.091), FAR of 0.332 (0.161), and fbias of 1.034 (1.084), respectively, when comparing predicted icing values with actual measurements on pylons. Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. Meteorological factors such as relative humidity (RH), 10 m wind speed (WS₁₀), 2 m temperature (T₂), and precipitation (PRE) demonstrated a range of positive and negative contributions consistent with the observed growth of icing. Thus, our multi-source remote-sensing data-fusion approach, combined with the stacking model, offers a highly accurate and interpretable solution for generating real-time icing grid fields. Full article

Sustainable land management in arid regions such as the Jordan Valley (JV) is essential as climate pressures and water shortages intensify. The extended use of treated wastewater (TWW) for irrigation, while necessary, brings potential risks related to the accumulation of trace elements and their impact on soil health and food safety. This study examined the spatial distribution, variability, and potential sources of five trace elements (Co, Hg, Mo, Mn, and Ni) in agricultural soils across a 305 km² area. A total of 127 surface soil samples were collected from fields irrigated with either TWW or freshwater (FW). Trace element concentrations were consistently higher in TWW-irrigated soils, although all values remained below WHO/FAO recommended thresholds for agricultural use. Spatial modeling was conducted using both ordinary kriging (OK) and empirical Bayesian kriging (EBK), with EBK showing greater prediction accuracy based on cross-validation statistics. To explore potential sources, semivariogram modeling, principal component analysis (PCA), and hierarchical clustering were employed. PCA, spatial distribution patterns, correlation analysis, and comparisons between TWW and FW sources suggest that Co, Mn, Mo, and Ni are primarily influenced by anthropogenic inputs, including TWW irrigation, chemical fertilizers, and organic amendments. Co exhibited a stronger association with TWW, whereas Mn, Mo, and Ni were more closely linked to fertilizer application. In contrast, Hg appears to originate predominantly from geogenic sources. These findings provide a foundation for improved irrigation management and fertilizer application strategies, contributing to long-term soil sustainability in water-limited environments like the JV. Full article

In low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with spatiotemporal stability. Spatiotemporal Stable Peak (SSP) maps were generated using the Ratio Vegetation Index (RVI) time-series data of rice and wheat, and they were used to represent crop growth information with spatiotemporal stability. Eighty-three soil sampling sites were arranged on the SSP maps with a regular grid. Ridge Regression, Ordinary Kriging, and Co-Kriging were adopted to map soil texture. The results showed that the SSP was closely related to clay and sand contents, with Pearson’s |r| ranging from 0.57 to 0.67. SSP-based Ridge Regression yielded better prediction accuracy (MAE = 3.95 and RMSE = 4.57) than Ordinary Kriging (MAE = 4.45 and RMSE = 5.19) in predicting clay content. The comparison between Ordinary Kriging and SSP-based Co-Kriging further demonstrated the effectiveness of SSP in improving clay content prediction accuracy, with an increase in R² of 70% and a reduction in RMSE of 3.85%. Similar results were obtained for sand content prediction. These results suggest that SSP can serve as an effective environmental variable for predicting soil texture spatial variation in low-relief agricultural areas. Full article

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) Overall, air pollutant concentrations in Zaozhuang decrease from 2018 to 2022, with NO₂, SO₂, PM_2.5, and PM₁₀ concentrations declining by 17.3%, 52.2%, 28.9%, and 33.6%, respectively. However, O₃ concentration increases by 2.5% in 2022 compared to 2018. Seasonally, SO₂, PM_2.5, and PM₁₀ concentrations are the highest in winter and lowest in summer, while CO, NO₂, and O₃ follow a winter > autumn > spring > summer pattern. Weekly variations show that daily average concentrations of CO, NO₂, SO₂, PM_2.5, and PM₁₀ peak on Mondays, with concentrations slightly higher on weekdays than weekends. (2) Spatially, CO, NO₂, PM_2.5, and PM₁₀ concentrations are higher in the southern region, while O₃ and SO₂ concentrations are elevated in Shizhong District, Xuecheng District, and Tengzhou City. (3) Correlation analysis reveals that meteorological parameters, such as precipitation, significantly influence pollutant concentrations, with precipitation playing a role in reducing pollutant levels. This study highlights the effectiveness of the Kriging method in analyzing the complex spatio-temporal dynamics of air pollutants, offering valuable insights for environmental policy and urban planning. Full article

Air pollution has become a major environmental issue, posing severe threats to human health and ecosystems. Accurately predicting future regional air quality is crucial for effective air pollution control and management strategies. This study proposes a novel deep learning-based approach. First, Kriging interpolation was applied to meteorological indicators such as temperature, humidity, and wind speed, as well as climate-altering gas indicators like CO₂, SO₂, and NO₂ recorded at monitoring stations to obtain their spatial distributions over the entire region. Subsequently, a long short-term memory neural network (SwinLSTM) incorporating Swin Transformer feature extraction was employed to learn the correlations from regional meteorological data and historical air quality records. This model overcomes the limitation of traditional CNNs by capturing long-range spatial dependencies when processing two-dimensional meteorological data through its sliding window attention mechanism. Ultimately, it outputs air quality predictions in both spatial and temporal dimensions. This study collected data from 29 stations across four cities surrounding China’s Dongting Lake for experimentation. Predictions for PM2.5 and PM10 levels over the entire lake area were made for 1, 6, and 24 h. The results demonstrate that the proposed SwinLSTM architecture significantly outperforms the current mainstream ConvLSTM architecture, with an average R-squared improvement of 5%, establishing a new state-of-the-art model for spatiotemporal air quality prediction. Full article

The concept of metal bioavailability in soils is increasingly becoming the key to addressing potential risks. Yet, space–time variations of heavy metal concentrations in salt-affected soils is still vague. The current work, therefore, is the first attempt to address spatial and seasonal analyses of heavy metals in a Mediterranean arid agroecosystem. This study was conducted in a coastal area in northeastern Egypt as an example. The DTPA-extractable concentrations of Cr, Co, Cu, Fe, Pb, Mn, Ni, and Zn in addition to the main properties of 70 georeferenced soil samples (0–30 cm) were determined during the wet (March) and dry (September) seasons. The results revealed that except for Cu, the concentrations of all the determined metals stood below the safe limits. On average, the concentrations of Cu were 4.1- and 5-fold the acceptable limit of 0.20 mg kg⁻¹, respectively. The statistical analysis indicated that seasonal variations greatly affect the concentrations of Mn, Ni, and Zn. Compared with the wet season, significant increases of 1.25, 1.50, and 1.28-fold in the concentrations of these metals occurred during the dry season, respectively. The principal component analysis affirmed that the presence of Cr, Co, Fe, and Ni was closely related to geogenic factors; meanwhile, agronomic practices were likely the main inputs of Cu, Pb, and Zn. The geostatistical analysis illustrated that the geographic variability of Cr, Fe, Mn, and Zn was due to interactions of natural and stochastic processes. Farming practices controlled the spatial variability of Ni, Pb (in the wet period), and Co (in the dry period). The effect of natural processes during the wet period was evident for Cu, which showed strong spatial variability. The kriged maps showed that the concentrations of Co, Fe, and Ni tended to increase seaward and were found to be affected by pH, salt ions, and exchangeable Na⁺. Moreover, both silt and organic matter content had profound impacts on the spatial distribution of Cr, while the distributions of Cu, Pb, and Zn were linked to that of CaCO₃ content. The suggested mechanisms governing metal bioavailability were sorption and complexation with ligands (for Co, Fe, and Ni), redox potential (for Cr), dissolution–precipitation (for Mn), and ion exchange (for Cu, Pb, and Zn). The results of this study affirm that drying–wetting cycles and spatial distribution affect the bioavailability of heavy metals in coastal salt-affected soils of arid regions. These findings imply that seasonality (wet and dry) and spatiality should be considered for monitoring and rehabilitation of degraded soils under similar ecological conditions. Full article

Heavy metal soil contamination is a global environmental issue that poses serious threats to human health, agricultural advancement, and ecosystem systems. Thirty-five soil samples from various parts of Jazan, Southwest Saudi Arabia, were collected. To create spatial pattern maps for nine potentially toxic elements (PTEs) (As, Co, Cr, Cu, Fe, Ni, Pb, V, and Zn), Ordinary Kriging (OK) was utilized. The variability of the soil metal concentration was estimated using multivariate analysis, including principal component analysis (PCA) and cluster analysis. In addition, the levels of soil contamination in the research area were assessed using contaminations indices, namely, Enrichment Factor (EF), Contamination Factor (CF), and geoaccumulation index (Igeo), and modified contamination degree (mC_d). Normalized Difference Vegetation Index (NDVI) and land use/land cover (LULC) were assessed to evaluate vegetation density and identify different forms of land cover and land use. The results showed that the Gaussian model fitted As well, whereas the spherical model fitted Co, Cr, Cu, Ni, and Zn. An exponential model was fitted to Fe and V. Pb also suited the Stable model. In each of the selected metals, the root mean square standardized error (RMSSE) values were close to one, and the mean standardized error (MSE) values were almost zero for each fitted model. Moreover, the findings showed that there was a tendency for the concentration of heavy metals in the research area to rise from west to east. The cluster analysis divided the data in this investigation into two clusters. Significant alterations in Co, Cr, Cu, Fe, Ni, V, and Zn were revealed by the acquired data. However, the total As and Pb concentrations in the two clusters did not differ significantly. The mC_d value of the research region often fell into one of three classes, with areas of 148.20 km² (nil to very low degree of contamination), 26.16 km² (low degree of contamination), and 0.495 km² (moderate degree of contamination). The findings indicated that the minerals connected to the Arabian Shield’s basement rocks are the main source of these PTEs. It is crucial to monitor PTEs contamination because the research region is highly cultivated, as shown by the NDVI and LULC status. Given the potential for future pollution due to human activity, PTEsPTEs decision-makers may use the findings of the spatial distribution maps of pollutants and their concentrations as a basis for future monitoring of PTEs concentrations in the study area. Full article

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

Accurate and precise prediction of actual evapotranspiration (AET) on a large scale is a fundamental issue in natural sciences such as forestry (especially in species selection and planning), hydrology, and agriculture. With the estimation of AET, controlling dams, agriculture, and irrigation and providing potable and utility water supply for industry would be possible. Gathering reliable AET data is possible only with a sufficient weather station network, which is rarely established in many countries like Turkey. Therefore, climate models must be developed for reliable AET data, especially in countries with complex terrains. This study aimed to generate spatiotemporal AET surfaces using the Australian National University spline (ANUSPLIN) model and compare the results with the maps generated by the inverse distance weighting (IDW) and co-kriging (KRG) interpolation techniques. Findings from the interpolated surfaces were validated in three ways: (1) some diagnostics from the surface fitting model include measures such as signal, mean, root mean square predictive error, root mean square error estimate, root mean square residual of the spline, and the estimated standard deviation of noise in the spline; (2) a comparison of common error statistics between the interpolated surfaces and withheld climate data; and (3) evaluation by comparing model results with other interpolation methods using metrics such as mean absolute error, mean error, root mean square error, and adjusted R² (R²_adj). The correlation between AET and normalized difference vegetation index (NDVI) was also evaluated. ANUSPLIN outperformed the other techniques, accounting for 73 to 94% (RMSE: 3.7 to 26.1%) of the seasonal variation in AET with an annual value of 83% (RMSE: 10.0%). The correlation coefficient between observed and predicted AET based on NDVI ranged from 0.49 to 0.71 for point-based and 0.62 to 0.83 for polygon-based data. The generated maps at a spatial resolution of 0.005° × 0.005° could provide valuable insights to researchers and practitioners in the natural resources management domain. Full article

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. Full article

Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO₃), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R² > 0.90), while CaCO₃ showed lower accuracy (R² = 0.67). Normalization of SOC, TN, and CaCO₃ did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management. Full article

Groundwater contamination with sub-lethal dissolved contaminants poses significant health risks globally, especially in rural India, where access to safe drinking water remains a critical challenge. This study explores the hydrogeochemical characterization and associated health risks of groundwater from shallow aquifers in the Marginal Ganga Alluvial Plain (MGAP) of northern India. The groundwater chemistry is dominated by Ca-Mg-CO₃ and Ca-Mg-Cl types, where there is dominance of silicate weathering and the ion-exchange processes are responsible for this solute composition in the groundwater. All the ionic species are within the permissible limits of the World Health Organization, except fluoride (F⁻) and nitrate (NO₃⁻). Geochemical analysis using bivariate relationships and saturation plots attributes the occurrence of F⁻ to geogenic sources, primarily the chemical weathering of granite-granodiorite, while NO₃⁻ contaminants are linked to anthropogenic inputs, such as nitrogen-rich fertilizers, in the absence of a large-scale urban environment. Multivariate statistical analyses, including hierarchical cluster analysis and factor analysis, confirm the predominance of geogenic controls, with NO₃⁻-enriched samples derived from anthropogenic factors. The spatial distribution and probability predictions of F⁻ and NO₃⁻ were generated using a non-parametric co-kriging technique approach, aiding in the delineation of contamination hotspots. The integration of the USEPA human health risk assessment methodology with the urbanization index has revealed critical findings, identifying approximately 23% of the study area as being at high risk. This comprehensive approach, which synergizes geospatial analysis and statistical methods, proves to be highly effective in delineating priority zones for health intervention. The results highlight the pressing need for targeted mitigation measures and the implementation of sustainable groundwater management practices at regional, national, and global levels. Full article

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties. Full article